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Ecology and Evolution Jun 2024Body size is a fundamental biological trait shaping ecological interactions, evolutionary processes, and our understanding of the structure and dynamics of marine...
Body size is a fundamental biological trait shaping ecological interactions, evolutionary processes, and our understanding of the structure and dynamics of marine communities on a global scale. Accurately defining a species' body size, despite the ease of measurement, poses significant challenges due to varied methodologies, tool usage, and subjectivity among researchers, resulting in multiple, often discrepant size estimates. These discrepancies, stemming from diverse measurement approaches and inherent variability, could substantially impact the reliability and precision of ecological and evolutionary studies reliant on body size data across extensive species datasets. This study examines the variation in reported maximum body sizes across 69,570 individual measurements of maximum size, ranging from <0.2 μm to >45 m, for 27,271 species of marine metazoans. The research aims to investigate how reported maximum size variations within species relate to organism size, taxonomy, habitat, and the presence of skeletal structures. The investigation particularly focuses on understanding why discrepancies in maximum size estimates arise and their potential implications for broader ecological and evolutionary studies relying on body size data. Variation in reported maximum sizes is zero for 38% of species, and low for most species, although it exceeds two orders of magnitude for some species. The likelihood of zero variation in maximum size decreased with more measurements and increased in larger species, though this varied across phyla and habitats. Pelagic organisms consistently had low maximum size range values, while small species with unspecified habitats had the highest variation. Variations in maximum size within a species were notably smaller than interspecific variation at higher taxonomic levels. Significant variation in maximum size estimates exists within marine species, and partially explained by organism size, taxonomic group, and habitat. Variation in maximum size could be reduced by standardized measurement protocols and improved meta-data. Despite the variation, egregious errors in published maximum size measurements are rare, and their impact on comparative macroecological and macroevolutionary research is likely minimal.
PubMed: 38840585
DOI: 10.1002/ece3.11506 -
BMC Public Health Jun 2024The East African Community (EAC) grapples with many challenges in tackling infectious disease threats and antimicrobial resistance (AMR), underscoring the importance of...
The East African Community (EAC) grapples with many challenges in tackling infectious disease threats and antimicrobial resistance (AMR), underscoring the importance of regional and robust pathogen genomics capacities. However, a significant disparity exists among EAC Partner States in harnessing bacterial pathogen sequencing and data analysis capabilities for effective AMR surveillance and outbreak response. This study assesses the current landscape and challenges associated with pathogen next-generation sequencing (NGS) within EAC, explicitly focusing on World Health Organization (WHO) AMR-priority pathogens. The assessment adopts a comprehensive approach, integrating a questionnaire-based survey amongst National Public Health Laboratories (NPHLs) with an analysis of publicly available metadata on bacterial pathogens isolated in the EAC countries. In addition to the heavy reliance on third-party organizations for bacterial NGS, the findings reveal a significant disparity among EAC member States in leveraging bacterial pathogen sequencing and data analysis. Approximately 97% (n = 4,462) of publicly available high-quality bacterial genome assemblies of samples collected in the EAC were processed and analyzed by external organizations, mainly in Europe and North America. Tanzania led in-country sequencing efforts, followed by Kenya and Uganda. The other EAC countries had no publicly available samples or had all their samples sequenced and analyzed outside the region. Insufficient local NGS sequencing facilities, limited bioinformatics expertise, lack of adequate computing resources, and inadequate data-sharing mechanisms are among the most pressing challenges that hinder the EAC's NPHLs from effectively leveraging pathogen genomics data. These insights emphasized the need to strengthen microbial pathogen sequencing and data analysis capabilities within the EAC to empower these laboratories to conduct pathogen sequencing and data analysis independently. Substantial investments in equipment, technology, and capacity-building initiatives are crucial for supporting regional preparedness against infectious disease outbreaks and mitigating the impact of AMR burden. In addition, collaborative efforts should be developed to narrow the gap, remedy regional imbalances, and harmonize NGS data standards. Supporting regional collaboration, strengthening in-country genomics capabilities, and investing in long-term training programs will ultimately improve pathogen data generation and foster a robust NGS-driven AMR surveillance and outbreak response in the EAC, thereby supporting global health initiatives.
Topics: Humans; Disease Outbreaks; Africa, Eastern; Genomics; High-Throughput Nucleotide Sequencing; Drug Resistance, Bacterial; Bacteria; Genome, Bacterial; East African People
PubMed: 38840103
DOI: 10.1186/s12889-024-18990-0 -
Scientific Data Jun 2024The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatial-resolution and dedicated to the cognitive...
The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatial-resolution and dedicated to the cognitive mapping of the human brain. It consists in the deep phenotyping of twelve individuals, covering a broad range of psychological domains suitable for functional-atlasing applications. Here, we present the inclusion of task data from both naturalistic stimuli and trial-based designs, to uncover structures of brain activation. We rely on the Fast Shared Response Model (FastSRM) to provide a data-driven solution for modelling naturalistic stimuli, typically containing many features. We show that data from left-out runs can be reconstructed using FastSRM, enabling the extraction of networks from the visual, auditory and language systems. We also present the topographic organization of the visual system through retinotopy. In total, six new tasks were added to IBC, wherein four trial-based retinotopic tasks contributed with a mapping of the visual field to the cortex. IBC is open access: source plus derivatives imaging data and meta-data are available in public repositories.
Topics: Humans; Brain; Brain Mapping; Magnetic Resonance Imaging; Motion Pictures; Visual Cortex
PubMed: 38839770
DOI: 10.1038/s41597-024-03390-1 -
Microbial Genomics Jun 2024is a leading cause of infections in immunocompromised individuals and in healthcare settings. This study aims to understand the relationships between phenotypic...
is a leading cause of infections in immunocompromised individuals and in healthcare settings. This study aims to understand the relationships between phenotypic diversity and the functional metabolic landscape of clinical isolates. To better understand the metabolic repertoire of in infection, we deeply profiled a representative set from a library of 971 clinical isolates with corresponding patient metadata and bacterial phenotypes. The genotypic clustering based on whole-genome sequencing of the isolates, multilocus sequence types, and the phenotypic clustering generated from a multi-parametric analysis were compared to each other to assess the genotype-phenotype correlation. Genome-scale metabolic network reconstructions were developed for each isolate through amendments to an existing PA14 network reconstruction. These network reconstructions show diverse metabolic functionalities and enhance the collective pangenome metabolic repertoire. Characterizing this rich set of clinical isolates allows for a deeper understanding of the genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences during infection.
Topics: Pseudomonas aeruginosa; Humans; Phenotype; Pseudomonas Infections; Genotype; Metabolic Networks and Pathways; Whole Genome Sequencing; Multilocus Sequence Typing; Genome, Bacterial; Genetic Variation
PubMed: 38836744
DOI: 10.1099/mgen.0.001259 -
Scientific Data Jun 2024Experts from 18 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the 37 trillion cells in the healthy human body. Information relevant...
Experts from 18 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the 37 trillion cells in the healthy human body. Information relevant for HRA construction and usage is held by experts, published in scholarly papers, and captured in experimental data. However, these data sources use different metadata schemas and cannot be cross-searched efficiently. This paper documents the compilation of a dataset, named HRAlit, that links the 136 HRA v1.4 digital objects (31 organs with 4,279 anatomical structures, 1,210 cell types, 2,089 biomarkers) to 583,117 experts; 7,103,180 publications; 896,680 funded projects, and 1,816 experimental datasets. The resulting HRAlit has 22 tables with 20,939,937 records including 6 junction tables with 13,170,651 relationships. The HRAlit can be mined to identify leading experts, major papers, funding trends, or alignment with existing ontologies in support of systematic HRA construction and usage.
Topics: Humans; Metadata; Cells
PubMed: 38834597
DOI: 10.1038/s41597-024-03416-8 -
Computerized Medical Imaging and... May 2024Lung cancer screening (LCS) using annual computed tomography (CT) scanning significantly reduces mortality by detecting cancerous lung nodules at an earlier stage. Deep...
Lung cancer screening (LCS) using annual computed tomography (CT) scanning significantly reduces mortality by detecting cancerous lung nodules at an earlier stage. Deep learning algorithms can improve nodule malignancy risk stratification. However, they have typically been used to analyse single time point CT data when detecting malignant nodules on either baseline or incident CT LCS rounds. Deep learning algorithms have the greatest value in two aspects. These approaches have great potential in assessing nodule change across time-series CT scans where subtle changes may be challenging to identify using the human eye alone. Moreover, they could be targeted to detect nodules developing on incident screening rounds, where cancers are generally smaller and more challenging to detect confidently. Here, we show the performance of our Deep learning-based Computer-Aided Diagnosis model integrating Nodule and Lung imaging data with clinical Metadata Longitudinally (DeepCAD-NLM-L) for malignancy prediction. DeepCAD-NLM-L showed improved performance (AUC = 88%) against models utilizing single time-point data alone. DeepCAD-NLM-L also demonstrated comparable and complementary performance to radiologists when interpreting the most challenging nodules typically found in LCS programs. It also demonstrated similar performance to radiologists when assessed on out-of-distribution imaging dataset. The results emphasize the advantages of using time-series and multimodal analyses when interpreting malignancy risk in LCS.
PubMed: 38833895
DOI: 10.1016/j.compmedimag.2024.102399 -
AMIA Joint Summits on Translational... 2024Electronic health record (EHR) documentation is a leading reason for clinician burnout. While technology-enabled solutions like virtual and digital scribes aim to...
Electronic health record (EHR) documentation is a leading reason for clinician burnout. While technology-enabled solutions like virtual and digital scribes aim to improve this, there is limited evidence of their effectiveness and minimal guidance for healthcare systems around solution selection and implementation. A transdisciplinary approach, informed by clinician interviews and other considerations, was used to evaluate and select a virtual scribe solution to pilot in a rapid iterative sprint over 12 weeks. Surveys, interviews, and EHR metadata were analyzed over a staggered 30 day implementation with live and asynchronous virtual scribe solutions. Among 16 pilot clinicians, documentation burden metrics decreased for some but not all. Some clinicians had highly positive comments, and others had concerns regarding scribe training and quality. Our findings demonstrate that virtual scribes may reduce documentation burden for some clinicians and describe a method for a collaborative and iterative technology selection process for digital tools in practice.
PubMed: 38827085
DOI: No ID Found -
AMIA Joint Summits on Translational... 2024The results of clinical trials are a valuable source of evidence for researchers, policy makers, and healthcare professionals. However, online trial registries do not...
The results of clinical trials are a valuable source of evidence for researchers, policy makers, and healthcare professionals. However, online trial registries do not always contain links to the publications that report on their results, instead requiring a time-consuming manual search. Here, we explored the application of pre-trained transformer-based language models to automatically identify result-reporting publications of cancer clinical trials by computing dense vectors and performing semantic search. Models were fine-tuned on text data from trial registry fields and article metadata using a contrastive learning approach. The best performing model was PubMedBERT, which achieved a mean average precision of 0.592 and ranked 70.3% of a trial's publications in the top 5 results when tested on the holdout test trials. Our results suggest that semantic search using embeddings from transformer models may be an effective approach to the task of linking trials to their publications.
PubMed: 38827077
DOI: No ID Found -
Ecology and Evolution Jun 2024To address our climate emergency, "we must rapidly, radically reshape society"-Johnson & Wilkinson, All We Can Save. In science, reshaping requires formidable technical...
To address our climate emergency, "we must rapidly, radically reshape society"-Johnson & Wilkinson, All We Can Save. In science, reshaping requires formidable technical (cloud, coding, reproducibility) and cultural shifts (mindsets, hybrid collaboration, inclusion). We are a group of cross-government and academic scientists that are exploring better ways of working and not being too entrenched in our bureaucracies to do better science, support colleagues, and change the culture at our organizations. We share much-needed success stories and action for what we can all do to reshape science as part of the Open Science movement and 2023 Year of Open Science.
PubMed: 38826171
DOI: 10.1002/ece3.11341 -
Heliyon May 2024Genital tuberculosis (GT) is an infection that can affect the female reproductive system, including the uterus, cervix, and ovaries.
INTRODUCTION
Genital tuberculosis (GT) is an infection that can affect the female reproductive system, including the uterus, cervix, and ovaries.
OBJECTIVE
To perform a scientometric exploration to analyze the spatiotemporal trend, evolution, and emerging patterns of scholarly output on GT and female infertility.
METHODS
An observational, descriptive, retrospective study employing a scientometric methodology was carried out. Metadata from scholarly articles spanning the years 1990-2022 were extracted from the Web of Science. The metadata from the chosen articles, totaling 172 manuscripts, were exported on May 17, 2023, in plain text format, which will allow the analysis and integration of the data in the software used.
RESULTS
We found at 111 sources and found 172 documents on tuberculosis and female infertility. We observed an average annual growth rate of 7.46 %, and the average age of the documents was 10.4 years. The dual overlay map showed the distribution of scientific publications on tuberculosis and female infertility. Journals on the left side of the map are cited mainly in the journals on the right. We found that Clinical Infectious Diseases and Lancet journals condensed patterns and trends in 1995, while the Indian Journal of Tuberculosis did so in 1996. Dheda K., Joubert JJ., and Wang Y. were the authors who had India, Iran, and China as their main affiliation, respectively, and they mainly published their studies in the "American Journal of Respiratory and Critical Care Medicine" and "Tropical Doctor," among others.
CONCLUSIONS
This bibliometric study examined different sources and found an average annual growth rate of 7.46 %. Each article received an average of 16.48 citations. Different collaborative networks between countries were observed. In addition, there was a steady growth in published research in the field of tuberculosis and female infertility.
PubMed: 38818201
DOI: 10.1016/j.heliyon.2024.e31396